Compressed object imaging method and system based on deep learning

Abstract

The invention discloses a compressed object imaging method and a system based on deep learning. The method comprises the following steps: training a deep learning network of a sample image to obtain afeature measurement matrix of a target object and a reconstruction sub-network of the target object, performing optical compression sampling on the target object, and importing the target object intothe reconstruction sub-network to perform target object reconstruction to complete compressed object imaging. According to the method, the limitation that only the whole shot scene is imaged by the traditional imaging technology is broken through; only the required specific target object is extracted; according to the technical scheme, the target object is separated from the background, other background objects are filtered out, optical compression imaging of the specific target object is completed, real-time monitoring, recognition and tracking of the target are facilitated, meanwhile, occupation of network bandwidth and storage expenditure of data in the transmission process are greatly reduced, the storage and transmission problems of mass data are solved, and the real-time performanceof the system is improved.

Description

technical field [0001] The present invention relates to technical fields such as optical imaging, computational imaging, target detection, recognition and tracking, and artificial intelligence applications, and in particular to a compression object imaging method and system based on deep learning. Background technique [0002] In complex physical scenes, how to simulate the biological visual system, quickly capture important image information, process and understand it, and actively screen out the target objects of interest has always been a research direction that scholars at home and abroad are keen on, and has important research value. . Especially in the digital age where a large amount of data streams are generated every day, how to make full use of the characteristics of high-speed computing of computers to quickly capture important image information and perform corresponding processing and understanding in the face of complex physical scenes, and actively Screening o...

AI Technical Summary

Problems solved by technology

[0004] However, most of the target recognition and detection methods currently proposed adopt the method of collecting first and then processing, which will cause the system to analyze and process a large amount of useless information, affecting the real-time performance of the system, and the massive data collected will be lost during storage and transmission. Put enormous pressure on system hardware and network bandwidth

Obviously, this method will be limited by the speed of information transmission and information processing, which will become a bottleneck restricting its development.

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